Archive for the ‘Bioinformatics’ Category

By combining a research technique that dates back 136 years with modern molecular genetics, a Johns Hopkins neuroscientist has been able to see how a mammal’s brain shrewdly revisits and reuses the same molecular cues to control the complex design of its circuits.

Students navigate microscopic world of immune system proteins and cells to save patient with bacterial infection

While navigating the microscopic world of immune system proteins and cells to save a patient suffering from a raging bacterial infection, young teenage players of the “Immune Attack” video game measurably improved their understanding of cell biology and molecular science, according to a study that will be presented at the American Society for Cell Biology (ASCB) 49th Annual Meeting, Dec. 5-9, 2009 in San Diego.

Remotely controlling the Microbot Explorer, named for its 25-micron diameter, the teenagers traveled through the bloodstream and connective tissue, interacting at the nanometer scale with receptors, hormones and lipids that have been drawn to appear like the schematics that scientists use in their own models.

Game actions, such as the capture of white blood cells by proteins on blood vessel walls, mimic activities that occur in nature.

“Immune Attack,” a “third person shooter,” three-dimensional video game, was devised by Melanie A. Stegman, Ph.D., and Michelle L. Fox of the Learning Technologies Program at the Federation of American Scientists in Washington, D.C.

The students’ knowledge, comprehension of game dynamics and confidence with the material were much higher than the 142 students who were tested after playing the Medical Mysteries Series video game, which covers non-molecular aspects of infectious disease.

“Additionally, we have used ‘Immune Attack’ to inspire high school computer programming classes to create their own new videos games based on ‘Immune Attack,'” Stegman added.

The first edition of “Immune Attack” is available for free download at www.ImmuneAttack.org. “Immune Attack 2.0” should be released in early 2010.

Students navigate microscopic world of immune system proteins and cells to save patient with bacterial infection

The correct combination of proteins is decisive for healthy aging, not reducing the calories in our diet.

A new study of the Max Planck Institute for Biology of Ageing could help to understand the positive effect of dietary restriction on healthy ageing. Previous evidence from different organisms (fruit flies and mice) have shown that dietary restriction increases longevity, but with a potential negative side effect of diminished fertility. So the female fruit fly reproduces less frequently with a reduced litter size on a low calorie diet, but its reproductive span lasts longer. This is the result of an evolutionary trait, as scientists believe: essential nutrients are diverted towards survival instead of reproduction. (Nature, December 3, 2009) Researchers from the newly founded Max Planck Institute for Biology of Ageing in Cologne have studied whether health benefit stem from a reduction in specific nutrients or calorie intake in general by manipulating the diet of female fruit flies. The fruit flies were fed a diet of yeast, sugar and water, but with differing amounts of key nutrients, such as vitamins, lipids and amino acids. The scientists were able to show that longevity and fertility are affected by a combination of the type and amount of amino acids; whilst varying the amount of the other nutrients had little or no effect. Furthermore, the researchers found out in previous studies that levels of a particular amino acid – methionine – were crucial to increasing lifespan without decreasing fertility. By carefully manipulating the balance of amino acids, both lifespan and fertility were maximised. For the first time, this indicates that it is possible to extend lifespan without wholesale dietary restriction and without lowering reproductive capacity. As the effects of dietary restriction on lifespan is evolutionary conserved – observed in different organisms – researchers believe that the essential mechanisms apply to it as well. Even though the human genome has about four times the number of genes as the fruit fly genome, there are many similarities on a genetic level, allowing these results to be of significance for humans as well.

Hierarchical clustering is a technique for grouping samples/data points into categories and subcategories based on a similarity measure. Being the powerful statistical package it is, R has several routines for doing hierarchical clustering.

Different libraries have different clustering functions

Package

Function

ctc

xcluster

amap

hcluster

amap

hclusterpar

stats

hclust

cluster

agnes

Xcluster is proven faster among rest of them.

I was working with a breast cancer microarray cell line data which was in .csv format. First I read the csv file as a matrix into R using

A <- as.matrix(read.csv(“breast_cancer.csv”, header=F))

Make sure there is no missing values in the matrix. I used an algorithm to replace the missing values. There are few inbuilt functions in R for replacing missing values using zeros, using mean, median and linear interpolation but they are not recommended for microarray data.

Then I clustered it using Xcluster

C <- xcluster(dist(A))

ctc library should have been installed prior to everything, else you will get an error.

Error: could not find function “xcluster”

Once the clustering is done, we can plot the results of our cluster analysis using this command:

plot(C)

If your dataset is small, this might work well for you, but for most genomics applications, you’ll get a tree-shaped fuzzball like this:

The solution to this is to load a library from the Bioconductor package, called “ctc”. This will let us export the cluster object in the Newick file format. It can then be imported into other more powerful graphing programs. This is done like so:

You now have a file in Newick format, but R puts quotes around the output for some annoying reason. Open the file in notepad and remove the quotes and it should be ready to use.

To get a better, more readable plot, download “Dendroscope” from the University of Tubingen. Dendroscope will let you import the Newick file you created and gives you extensive plotting options. Check out this wicked Circular Cladogram…

There are lots of options for computing the clustering, and they may give very different results, so proceed with caution, but in general hierarchical clustering can be a useful tool for lots of data analysis situations.

I did this experiment as a part of my project and part of this tutorial was obtained from getting genetics done blog.